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--- |
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language: en |
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license: mit |
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library_name: diffusers |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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datasets: yuntian-deng/im2latex-100k |
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metrics: [] |
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--- |
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# latex2im_ss_finetunegptneo |
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## Model description |
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Details of this model can be found in [our paper on markup-to-image generation](https://arxiv.org/pdf/2210.05147.pdf). Our code is built on top of HuggingFace [diffusers](https://github.com/huggingface/diffusers) and [transformers](https://github.com/huggingface/transformers). |
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## Online Demo: [https://huggingface.co/spaces/yuntian-deng/latex2im](https://huggingface.co/spaces/yuntian-deng/latex2im). |
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## Model Details |
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- **Developed by:** Yuntian Deng, Noriyuki Kojima, Alexander M. Rush |
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- **Model type:** Diffusion-based text-to-image generation model |
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- **Language(s):** English |
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- **License:** [MIT](https://github.com/da03/markup2im/blob/main/LICENSE). |
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- **Model Description:** This is a model that can be used to generate math formula images based on LaTeX prompts. |
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- **Resources for more information:** [GitHub Repository](https://github.com/da03/markup2im), [Paper](https://arxiv.org/abs/2210.05147). |
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- **Cite as:** |
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@inproceedings{ |
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deng2023markuptoimage, |
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title={Markup-to-Image Diffusion Models with Scheduled Sampling}, |
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author={Yuntian Deng and Noriyuki Kojima and Alexander M Rush}, |
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booktitle={The Eleventh International Conference on Learning Representations }, |
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year={2023}, |
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url={https://openreview.net/forum?id=81VJDmOE2ol} |
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} |
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